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Causal Inference Machine Learning Postdoctoral Jobs

Upenn

$85K/yr

Postdoctoral Positions: Perelman School of Medicine PostdoctoralLocation University of Pennsylvania - School of Medicine Open DateDescription Department: The Center for Causal Inference (CCI) in the ...

The Voleon Group

Member of Research Staff, Causal Inference, Voleon Securities Location Employment Type Full time ... and machine learning research as well as highly experienced finance and technology professionals.

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Causal Inference Machine Learning Postdoctoral information

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$35.5K

$54.2K

$61K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Sep 15, 2026, the average yearly pay for causal inference machine learning postdoctoral in the United States is $54,223.00, according to ZipRecruiter salary data. Most workers in this role earn between $53,500.00 and $56,500.00 per year, depending on experience, location, and employer.

What is a causal inference machine learning postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

Is it difficult to get a causal inference machine learning postdoctoral position?

Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.
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Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $54,223 per year, or $26.1 per hour.

Machine Learning Scientist 5 - Localization

New York, NY • On-site

Netflix
Arts, Entertainment, and Recreation • 5 - 10K employees
Learn more about Netflixexternal

Full-time

Medical, Life, Retirement, PTO

Posted 28 days ago

Netflix rating

5.8

Company rating: 5.8 out of 10

Based on 15 frontline employees who took The Breakroom Quiz

Job description

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what's next.
The Localization Data Science and Engineering team is at the forefront of removing language barriers and providing a stellar member experience to all our members regardless of their language preferences. We are responsible for the translation and cultural adaptation of all aspects of member interaction, including beautiful localized user interfaces, subtitles, and dubbing of award-winning Netflix originals.
We are looking for an experienced Machine Learning Scientist to join our growing team. In this role, you will build causal and machine learning models to evaluate the impact of localization algos, partner with teammates to support localization algo strategy, and train supervised ML models for localization use cases. You will also partner with a talented cross-functional team of engineers, scientists, product managers, and domain experts to shape localization strategy and deliver business impact.
Responsibilities
  • Act as strategic partner for researchers and engineers to guide localization algo development
  • Define and execute on roadmaps for measuring localization member impact and improving localization member experience with Causal Inference and Machine Learning tools
  • Partner closely with other business leaders, product managers, and other data scientists to refine and scale your findings
  • Present your research and insights to all levels of the company
  • Become a regional expert on Localization Data Science and Engineering, helping educate and connect with regional offices

About you
  • Proven track record of researching and leading Causal Inference, Machine Learning, and AI Evaluation methods in ambiguous and complex areas with a focus on technical rigor and robustness
  • High proficiency in standard tech stack (e.g., R, Python, SQL), Causal Inference (e.g., propensity score matching, double machine learning), and Machine Learning (Supervised Learning, LLM Evaluation methods)
  • 4+ years of relevant experience with Causal Inference and Machine Learning applications
  • Exceptional communication and collaboration skills coupled with strong business acumen
  • Comfortable with ambiguity; able to take ownership, and thrive with minimal oversight and process
  • Netflix culture resonates with you

Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here.
Netflix is a unique culture and environment. Learn more here.
Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner.
We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.

What Netflix employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

Netflix logo

About Netflix

Sourced by ZipRecruiter

Netflix is the world's leading streaming entertainment service with 222 million paid memberships in over 190 countries enjoying TV series, documentaries, feature films and mobile games across a wide variety of genres and languages. Members can watch as much as they want, anytime, anywhere, on any Internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments.

Industry

Arts, entertainment, and recreation

Company size

5,001 - 10,000 Employees

Headquarters location

Los Gatos, CA, US

Year founded

1997

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